Search results

This chapter introduces Marx's theory of the determination of profit rates. It contrasts this theory with what happened in the late nineteenth century to British profit rates with a detailed statistical account. It identifies missing features in the standard presentation and contrasts these with the overaccumulation hypothesis that he presents elsewhere. A formal mathematical model using the overaccumulation hypothesis is then given and tested against modern empirical data.

Negative brand engagement represents a pervasive and persistent feature of interactivity in online contexts. Although existing research suggests that consumer negativity is potentially more impactful or detrimental to brands than its positive counterpart, few studies have examined negative brand-related cognitions, feelings and behaviours. Building on the concept of brand engagement, this study aims to operationalise negative online brand engagement.

This paper presents the results of nine studies that contributed to the development and validation of the proposed scale. Building on the concept of engagement, Studies 1–3 enhanced the construct conceptualisation and generated items. Study 4 involved validation with an academic expert panel. The process of measure operationalisation and validation with quantitative data was completed in Studies 5–8. Finally, the scale's nomological validity was assessed in Study 9.

The results confirm the multidimensional nature of negative online brand engagement. The validated instrument encompasses four dimensions (cognition, affection, online constructive behaviour and online destructive behaviour), captured by 17 items.

Progress in understanding and dealing with negative online brand engagement has been hampered by disagreements over conceptualisation and the absence of measures that capture the phenomenon. This work enhances managerial understanding of negativity fostering strategies that protect brand engagement and improve firm performance.

To run a job guarantee public policy scheme, it is important to know the aspiration level or the reference point of labor, and accordingly, the labor hour and the wage sequence are to be prepared. The existing job guarantee schemes consider the same wage rates for all types of jobs. As a result, it is to identify the reference point. The present work aims to propose a job guarantee scheme where different types of jobs have different wage rates. The paper explains the choice problem between labor and leisure at different wage rates and proposes complete computational tools to be incorporated into the job guarantee schemes. The paper also gives a mechanism to prepare the list of jobs and corresponding wage rates by maintaining a balance between labor and leisure, where productive activities measure labor hours and labor welfare measures leisure hours. Lastly, the paper provides the analytical tools to interpret the ex-post data of the job guarantee public policy schemes.

The paper has been written based on the Coordination Game and its Welfare Implications in the job guarantee public policy schemes.

The present paper gives an initial work to measure the choice between labor and leisure for the different wage rates practically. This will help in getting the equilibrium strategies, namely, the combination of the labor hour and the wage rate between the policymaker and the labor. This method will help to implement the job guarantee schemes. For example, to run successfully the Basic Income policy, the basic income calculation should give due care; otherwise, there will be a downward trend in the basic income and the welfare of labor will be reduced, because the labor would have to supply excess labor to meet the target income.

This paper derives theories and explains how the equilibrium in this coordination game can be achieved. The paper explains how the policy of the job guarantee schemes can be practiced practically. In the MGNREGA scheme, the public institution declares different categories of jobs with different wage rates. The categories have been classified with respect to the hours required to complete the job. Therefore, the public institution declares different lists or a sequence of pairs of labor hours and wage rates. Moreover, the list is stochastic, because the list can be changed by the inclusion of an offer from the market as well. The labor has to select from the list. The challenge on the part of the public institution is to prepare the list in such a way so that the inclusion of the market offers will not distort the equilibrium of the coordination game. An important method has been proposed here to analyze the ex-post data of job offers so that the preparation of the future sequence of the job offers can be prepared with due care. One objective of the policymaker here is to make a list of job offers in such a way so that the labor supply will be converging to a point and that will not deviate if the wage rate increases further. This objective will make a balance of the distribution of funds between the existing registered labor and the new entrants into the job guarantee schemes.

The alienation of megaproject environmental responsibility (MER) behavior is destructive, but its mechanism has not been clearly depicted. Based on fraud triangle theory and the fuzzy set qualitative comparative analysis (fsQCA) method, this study explored the combined effect of antecedent factors on alienation of MER behavior.

Based on the fraud triangle theory and literature review, eight influencing factors associated with the alienation of MER behavior were first identified. Subsequently, the fuzzy-set qualitative comparative analysis was used in this study to reveal configurations influencing alienation of MER behavior.

The study found nine configurations of MER behavioral alienation antecedent factors, integrated into three types of driving modes, i.e. “economic pressure + learning effect,” “institutional defect + moral rejection,” and “information asymmetry + economic pressure + expectation pressure.”

By analyzing the configuration effects of various induced conditions, this study puts forward a comprehensive analysis framework to solve the alienation of MER behavior in the megaprojects and a practical strategy to control alienation of MER behavior.

This study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.

To determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.

The FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.

The models are presented for static multiple crack growth problems. No material anisotropy is observed.

In practical crack-growth analyses, the NN approach provides significant benefits and savings.

The proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.

With the growth and profound influence of technology on our life, it is important to address the ethical issues inherent to the development and deployment of technology. Researchers and practitioners submit the need to inspect: how technology and ethics interact, how ethical principles regulate technology and what could be the probable future course of action to execute techno-ethical practices in a socio-technical discourse effectively. To address the thoughts related to techno-ethics, the authors of the present study conducted exploratory research to understand the trend and relevance of technology ethics since its inception.

The study collected over 679 documents for the period 1990–2022 from the Scopus database. A quantitative approach of bibliometric analysis was conducted to study the pattern of authorship, publications, citations, prominent journals and contributors in the subject area. VOS viewer software was utilized to visualize and map academic performance in techno-ethics.

The findings revealed that the concept of techno-ethics is an emerging field and requires more investigation to harness its relevance with everchanging technology development. The data revealed substantial growth in the field of techno-ethics in humanities, social science and management domain in the last two decades. Also, most of the prominent cited references and documents in the database tend to cover the theme of Artificial Intelligence, Big data, computer ethics, morality, decision-making, IT ethics, human rights, responsibility and privacy.

The article provides a comprehensive overview of scientific production and main research trends in techno-ethics until 2022. The study is a pioneer in expanding the academic productivity and performance of embedding ethics in technology.

Supply chains are under increased scrutiny as consumers have become aware of the dark side of downstream production practices. Many articles and exposés have led consumers to be more conscious of purchasing products from companies who source materials in a socially responsible and ethical manner. As a result, business-to-business (B2B) and business-to-consumers (B2C) companies are under increased pressure to source raw materials in a transparent and ethical way. Because of the associated costs, companies then look to benefit from increased brand equity by promoting to consumers how ethical their products are. The purpose of this study is to look at the case of the food industry to analyze sustainability messaging on Twitter in both B2B and B2C companies to determine which of the dimensions of sustainability (people, profit, planet) are being emphasized.

In this study, two published dictionaries were combined to capture the three dimensions of “sustainability,” and these scales were then used to analyze Twitter posts. This study created a unique software package to classify, mine, collect and analyze Twitter data. This study used these tools to analyze 246,386 Twitter posts within a sample of 39 leading B2C and B2B food companies over a 10-year period (2012–2021) to explore brand messaging and engagement rate.

This research revealed several interesting results. These include how B2B companies have emphasized the employee (people) dimensions of sustainability, while B2C companies have had a more balanced approach that overall has prioritized the economic dimension (profit) of sustainability. The data on audience engagement revealed a mismatch between the types of sustainability messaging B2B companies and B2C companies in the food industry are posting and what engages audiences.

This study fills several gaps, including analyzing how B2B and B2C companies use sustainability language in their social media brand management, as well as looking at which dimensions of sustainability they emphasize and which ones engage audiences the most. This research is also novel in combining multiple existing scales under one project to analyze the triple bottom line in the analysis of qualitative texts.

The purpose of this study is to explore the synthesis of novel conductive photo-resins to produce flexible conducting composites for use in additive manufacturing. By using direct ink writing (DIW) additive manufacturing, this study aims to explore the fabrication of multimaterial devices with conductive and insulating components. Using digital light processing (DLP) additive manufacturing, this study aims to fabricate detailed objects with higher resolution than material extrusion 3D printing systems.

In this paper, several photocurable conducting resins were prepared for DIW and DLP additive manufacturing. These resins were then cured using 405 nm near UV light to create intrinsically conductive polymer (ICP) composites. The electrochemical properties of these composites were analysed, and the effect of co-monomer choice and crosslinking density was determined. These results determined a suitable resin for subsequent additive manufacture using DIW and DLP. These 3D printing techniques were used to develop flexible conducting devices of submillimetre resolution that were fabricated with unmodified, commercially available 3D printers.

Cyclic voltammetry and volume conductivity analysis of the conducting resins determined the most conductive resin formula for 3D printing. Conductive devices were fabricated using the two 3D printing techniques. A multimaterial soft conducting device was fabricated using DIW, and each conducting component was insulated from its neighbours. DLP was used to fabricate a soft conducting device with good XY resolution with a minimum feature size of 0.2 mm. All devices were prepared in unmodified commercially available 3D printers.

These findings have value in the development of soft robotics, artificial muscles and wearable sensors. In addition, this work highlights techniques for DIW and DLP additive manufacturing.

Several original conducting resin formulae were developed for use in two 3D printing systems. The resulting 3D-printed composites are soft and flexible while maintaining their conductive properties. These findings are of value to both polymer chemists and to the field of additive manufacturing.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.